Embedding Big Data in Risk Management. David Socha. Practice Partner, Industrial IoT Group, Teradata International
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1 Embedding Big Data in Risk Management David Socha Practice Partner, Industrial IoT Group, Teradata International
2 Big Data & Analytics in Risk Management David Socha, Practice Partner, Industrial IoT Group July 2017
3 If you woke up as an industrial company today, you will wake up as a software and analytics company tomorrow Jeff Immelt, CEO GE 3
4 Teradata was Big Data before there was Big Data Donald Feinberg, Gartner Teradata
5 Data-driven credit risk management Situation Problem Solution Impact Scana need to manage credit risk with high-risk commercial and industrial (C&I) customers They do this through analysis of payment history, propensity to pay and other economic data In this sector, there is higher financial risk per account and more sophisticated analysis required than for residential collections Excel spreadsheets used to manually analyze customer risk A highly manual process that took multiple people multiple weeks to complete Less than 1/3 of 1% of C&I customers could be analyzed at a time. Not less that a third. Less than a third of one percent No, seriously A new Teradata integrated data & analytics environment Automated analysis of 100% of C&I customers on a monthly basis A Watch List of 2000 high-risk customers monitored continuously 40% - 50% improvement in corporate collections $8M improvement in first year $45M improvement over 5 year review period Saves over 2800 man-hours annually Avoids hiring of additional headcount Teradata
6 The art of analytics Teradata
7 The art of analytics Teradata
8 Teradata
9 Teradata
10 Teradata
11 Teradata
12 Teradata
13 Teradata
14 Teradata
15 Teradata
16 Teradata
17 Data-driven credit risk management Situation Problem Solution Impact Scana need to manage credit risk with high-risk commercial and industrial (C&I) customers They do this through analysis of payment history, propensity to pay and other economic data In this sector, there is higher financial risk per account and more sophisticated analysis required than for residential collections Excel spreadsheets used to manually analyze customer risk A highly manual process that took multiple people multiple weeks to complete Less than 1/3 of 1% of C&I customers could be analyzed at a time. Not less that a third. Less than a third of one percent No, seriously A new Teradata integrated data & analytics environment Automated analysis of 100% of C&I customers on a monthly basis A Watch List of 2000 high-risk customers monitored continuously 40% - 50% improvement in corporate collections $8M improvement in first year $45M improvement over 5 year review period Saves over 2800 man-hours annually Avoids hiring of additional headcount Teradata
18 Data-driven credit risk management Situation Problem Solution Impact Scana need to manage credit risk with high-risk commercial and industrial (C&I) customers They do this through analysis of payment history, propensity to pay and other economic data In this sector, there is higher financial risk per account and more sophisticated analysis required than for residential collections Excel spreadsheets used to manually analyze customer risk A highly manual process that took multiple people multiple weeks to complete Less than 1/3 of 1% of C&I customers could be analyzed at a time. Not less that a third. Less than a third of one percent No, seriously A new Teradata integrated data & analytics environment Automated analysis of 100% of C&I customers on a monthly basis A Watch List of 2000 high-risk customers monitored continuously 40% - 50% improvement in corporate collections $8M improvement in first year $45M improvement over 5 year review period Saves over 2800 man-hours annually Avoids hiring of additional headcount Teradata
19 13 years on Teradata
20 20
21 David Socha Practice Partner, Industrial IoT Group Teradata International My montly blog on metering.com My blog on Forbes My quarterly blog on Teradata.com Follow me on My profile Teradata
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